Report generated on 2020-02-25, 12:44 based on data in:
/mnt/research/ShadeLab/GLBRC/mapping/metaT/fullAssembly/trimStats
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from IPython.display import Image
from IPython.core.display import HTML
from ipywidgets import Text, Dropdown, Output, interact
from matplotlib import pyplot as plt
from pandas import DataFrame, merge, read_csv, Series, to_datetime
import cufflinks as cf
import qgrid
cf.go_offline()
# cf.set_config_file(offline=False, world_readable=True)
cf.set_config_file(offline=True, world_readable=True)
### nbi:hide_in
Step 1. Split JGI files in PE files
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# <!-- # #HIDDEN
# # tImages = {"MetaG":Image("images/TrimReport_MetaG.png"),"MetaT":Image("images/MetaT_trimmomatic_plot.png")}
# # @interact
# # def trimReport(Source=["MetaG","MetaT"]):return tImages[Source]
# <img src="images/TrimReport_MetaG.png"></img>
# -->
HTML("html/MetaG_TrimStats.html")
Step 1. Align reads to respective host reference assembly
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HTML(filename="html/BothPerc.html")
Step 1. Extract reads that don't align the plant assembly
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HTML(filename='html/FungalAlign.html')
The annotations were performed using KEGG's prokaryotic peptides and eukaryotic peptides

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HTML(filename="html/MAG_Stats.html")
All bins with at least 50% completeness were used in a combined assembly. The analysis below comes from the counts for each sample. These counts were normalized by the number of reads that did not align to the fungal or host assemblies.
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Call: adonis(formula = dist.otu ~ map_16S\$time_numeric)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$time_numeric | 1 | 1.9356 | 1.93562 | 47.814 | 0.26298 | 0.001 *** |
| Residuals | 134 | 5.4246 | 0.04048 | 0.73702 | ||
| Total | 135 | 7.3603 | 1.000000 |


Call: adonis(formula = dist.otu ~ map_16S\$treatment)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$treatment | 1 | 0.0289 | 0.028930 | 0.52878 | 0.00393 | 0.755 |
| Residuals | 134 | 7.3313 | 0.054711 | 0.99607 | ||
| Total | 135 | 7.3603 | 1.000000 |
Call: adonis(formula = dist.otu ~ map_16S\$plant)
Permutation: free Number of permutations: 999
Terms added sequentially (first to last)
| Df | SumsOfSqs | MeanSqs | F.Model | R2 | Pr(>F) | |
|---|---|---|---|---|---|---|
| map_16S$plant | 1 | 0.1617 | 0.161704 | 3.0101 | 0.02197 | 0.027 * |
| Residuals | 134 | 7.1986 | 0.053721 | 0.97803 | ||
| Total | 135 | 7.3603 | 1.000000 |
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HTML("html/MetaT_TrimStats.html")
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HTML("html/MetaT_HostAlignment.html")
Link to the interactive tables. Static tables shown below.
In order for this to progress, 1 had to be complete. The next step is figure out why the metadata for the MetaT is not meshing with the R script.
Kallisto has an arguement to export a bam file when it runs, but I do not know how these bam files work or if they are the same thing as a bam from bowtie2. I am working with kallisto to get a bam file now and then I will see if that file works to extract annotations from the contigs